Tag Archives: Apple’s Siri

AI Personal Assistants are “Life Partners”

Artificial Intelligent (AI)  “Assistants”, or “Bots” are taken to the ‘next level’ when the assistant becomes a proactive entity based on the input from human intelligent experts that grows with machine learning.

Even the implication of an ‘Assistant’ v.  ‘Life Partner’ implies a greater degree of dynamic, and proactive interaction.   The cross over to becoming ‘Life Partner’ is when we go ‘above and beyond’ to help our partners succeed, or even survive the day to day.

Once we experience our current [digital, mobile] ‘assistants’ positively influencing our lives in a more intelligent, proactive manner, an emotional bond ‘grows’, and the investment in this technology will also expand.

Practical Applications Range:

  • Alcoholics Anonymous Coach , Mentor – enabling the human partner to overcome temporary weakness. Knowledge,  and “triggers” need to be incorporated into the AI ‘Partner’;  “Location / Proximity” reminder if person enters a shopping area that has a liquor store.  [AI] “Partner” help “talk down”
  • Understanding ‘data points’ from multiple sources, such as alarms,  and calendar events,  to derive ‘knowledge’, and create an actionable trigger.
    • e.g. “Did you remember to take your medicine?” unprompted; “There is a new article in N periodical, that pertains to your medicine.  Would you like to read it?”
    • e.g. 2 unprompted, “Weather calls for N inches of Snow.  Did you remember to service your Snow Blower this season?”
  • FinTech – while in department store XYZ looking to purchase Y over a certain amount, unprompted “Your credit score indicates you are ‘most likely’ eligible to ‘sign up’ for a store credit card, and get N percentage off your first purchase”  Multiple input sources used to achieve a potential sales opportunity.

IBM has a cognitive cloud of AI solutions leveraging IBM’s Watson.  Most/All of the 18 web applications they have hosted (with source) are driven by human interactive triggers, as with the “Natural Language Classifier”, which helps build a question-and-answer repository.

There are four bits that need to occur to accelerate adoption of the ‘AI Life Partner’:

  1. Knowledge Experts, or Subject Matter Experts (SME) need to be able to “pass on” their knowledge to build repositories.   IBM Watson Natural Language Classifier may be used.
  2. The integration of this knowledge into an AI medium, such as a ‘Digital Assistant’ needs to occur with corresponding ‘triggers’ 
  3. Our current AI ‘Assistants’ need to become [more] proactive as they integrate into our ‘digital’ lives, such as going beyond the setting of an alarm clock, hands free calling, or checking the sports score.   Our [AI] “Life Partner” needs to ‘act’ like buddy and fan of ‘our’ sports team.  Without prompting, proactively serve up knowledge [based on correlated, multiple sources], and/or take [acceptable] actions.
    1. E.g. FinTech – “Our schedule is open tonight, and there are great seats available, Section N, Seat A for ABC dollars on Stubhub.  Shall I make the purchase?”
      1. Partner with vendors to drive FinTech business rules.
  4. Take ‘advantage’ of more knowledge sources, such as the applications we use that collect our data.  Use multiple knowledge sources in concert, enabling the AI to correlate data and propose ‘complex’ rules of interaction.

Our AI ‘Life Partners’ may grow in knowledge, and mature the relationship between man and machine.   Incorporating derived rules leveraging machine learning, without input of a human expert, will come with risk and reward.

Alzheimer’s Inflicted: Technology to Help Remember Habitual Activities  

Anyone ever walk into a room and forget why on Earth you were there?  Were you about to get a cup of coffee, or get your car keys?  Wonderful!  It’s frustrating on my level of distraction, now magnify that to the Nth degree, Alzheimer’s.  Apply a rules and Induction engine, and poof!  A step further away from a managed care facility.

Teaching the AI Induction and rules engine may require the help of your 10 year old grandson.  Relatively easy,  you might need your grandson to sleep over for a day or two.

It’s all about variations of the same theme, tag a location, a room in an apartment, also action tag, such as getting a cup of coffee from the kitchen.  The repetitive nature of the activities with a location tag draws conclusions based on historical behavior.  The more variations of action and coinciding location tags, will begin to become ‘smarter’ about your habitual activities.  In addition, the calculations create a bell curve, a way to prioritize the most probable Location/Action tags used for the suggested behavior.    The ‘outliers’ on the bell curve will have the lowest probability of occurrence.

In addition, RFID tags installed in your apartment will increase the effectiveness of the ‘advice’ engine by adding more granular location tags.

Microchip_rfid_rice
Microchip RFID compared to the size of a grain of rice.
Beyond this ‘black box’ small, lightweight computer (smartphone) integrate a Bluetooth, NFC, WiFi antenna, a mobile application and you’re set.  A small, high quality Bluetooth microphone to interact with the app.  There’s also potential for exploring beyond the home.

Kidding, you don’t need that Grandson to help.  Speak into the mic, “Train” go into the room and say your activity, coffee.  This app will correlate your location, and action.  Everyone loves to be included in the Internet of Things, so app features like alerts for deviation from the location ‘map’ are possible.

In earnest, I am mostly certain that this type of solution exists.  Barriers to adoption could be computer/ smartphone generational gap.  Otherwise, someone is already producing the solution, and I just wasted a bus ride home.

Additionally, this software may be integrated with Apple’s Siri, Google Now,  Yahoo Index, Microsoft Cortana,  an extension of the Personal Assistant.